Any company, no matter the size of industry, has to have a firm grip on reality associated with data quality and integrity from day one.  There will be many high priority issues of running a sound business but this sits at the top of the list for importance.  One of the primary reasons companies go under is that instead of identifying appropriate strategies for data first and then implementing and managing systems to keep everything in check, they skip over this vital step and end up spending a tremendous amount of money trying to play catch up or fixing problems.

When this happens, the company begins to lose revenue and before they know it, they are in serious financial trouble.  Of course, when it comes to data quality and integrity, not only is there the money side to the equation, but the company has also been in operation without having the necessary tools and procedures in place to ensure data is safe and secure.  Therefore, the business is now faced with two issues, one being financial and the other being the risk of exposing proprietary information.

The truth is that while there are some options for getting things under control after the fact, data quality and integrity challenges are rarely managed in an effective manner once the problems begin.  Every successful company knows the key to full success is by starting with a comprehensive and controlled system.  This way, damage is avoided, thus reducing the level of risk for fixing the problem.

The best approach is to start by meeting with key personnel, as well as vendors and programmers who can offer the best solution for your data quality and integrity before the business opens.  Together, all of the needed rules, procedures, policies, monitoring, and constraints pertaining to the company’s data can be addressed and a plan developed and then put into motion.  As a result, making hasty decisions and spending a lot of money to fix a problem is completely avoided.

Working closely with a professional group that designs systems specific to data quality and integrity, all the guesswork on how to proceed is removed.  These professionals will guide you through the process of what it takes to create and grow an effective organization without feeling completely overwhelmed.  Remember, if the company’s data is protected, managed, and monitored appropriately, you are well on your way to the top.

Companies that offer solutions for data quality and integrity will give you all of the steps needed to build a foundation.  Some of the key areas that would be discussed prior to any system being implemented would include:

• All aspects of data quality and integrity would be explored, regardless how simple or complex so a proactive approach can be developed
• The major components relating to data lifecycle would also be addressed so the technical team can put together suggestions as to the tools required
• Any potential and existing risks would also be identified and then a step-by-step guide written for the launch of any database management solution
• Various products and resources would also be considered so data quality and integrity would be supported to include management, monitoring, cleansing, and warehousing

It is imperative that any business owner take the time and effort to fully understand how data quality and integrity can either help the company grow or cause it to fail.  Without a well designed strategy, companies start by capturing poor quality data and then having no sound system to manage it.  The two combined is a recipe for disaster.  Instead, a professional company will look at your specific needs so an affordable and realistic strategy can be presented.

At that stage, you would have the opportunity to look at the various technological options for maintaining data quality and integrity and make a decision based on need, support, tools, and cost.  With an effective plan in place, employees gather pertinent information, in putting the data into the system with little to no error.  After good data is stored, you then have everything you need to maintain the integrity.

Most present day business firms prefer to divide their functions into several highly character-specific “projects”. The success or failure of a company depends critically on the viability of the projects it chooses to undertake. In order to understand why projects fail and what corrective measures need to be taken in such scenarios, we need to understand “projects” clearly first:

A project can be defined as “any outcome the company or one of its team is committed to achieve that will take more than one action step to complete”. Alternatively, projects can also be explained as a management environment that is created for the purpose of delivering one or more business products according to a specified business case.

However, project management, on its own; do not constitute the sole important architecture practice of a successful corporate business house. Apart from management of projects, the most important analytical tasks of a business firm are:

• Databases, Appliances & Emerging Technology,
• Architecture & Implementation Consulting,
• Training and Mentoring personnel to help them experts in modeling the available data.

• Bachman diagrams,
• Barker’s Notation,
• Business rules
• Data Vault Modeling ,
• Entity-relationship model
• Extended Backus–Naur form,
• IDEF1X,
• Object-relational mapping,
• Object Role Modeling ,
• Relational Model, and
• Semantic data modeling

1. Data: This lies at the base level of the pyramid and consists of raw facts and figures coming into the possession of companies,
2. Information: The relevant part of the received data is then filtered out. This part is called information,
3. Knowledge: After analyzing the information, certain inferences and conclusions can be drawn. This is called the knowledge gleaned from the information, and
4. Wisdom: At the topmost level lies wisdom – the strategic plans of action that can be drawn up on the basis of the obtained inferences.

• Improving profitability,
• Reducing cost, and
• Improving customer satisfaction

• it must recognize change as a constant,
• it should ideally take incremental development approach,
• it should allow existing applications to continue working, and
• It needs to allow more data and new types of data to be added.

Architecture practices, at the highest level, must meet the requirements of the two distinct domains of IT-related activities – on-line transaction processing (OLTP) and business intelligence systems (BIS).

Data cleansing can be performed in two ways – manually and through software.

Manual data cleansing involves a person or better still with at least two people – one reading through the information and the other doing the necessary corrections. Typographic mistakes and spelling errors are checked and corrected, mislabeled data is appropriately labeled and filed, while unfinished or missing entries are subjected to research and rejuvenation. Unrecoverable records are simply dumped out for creating useful storage space. The mission is sometimes also called Data Scrubbing.

Now let us see what data cleansing is with a software solution. With large organizations (it is difficult for a large organization to carry out manual data cleansing simply because of the huge amount of data that might need to be verified and cleaned), data cleansing is often performed with the aid of computer software that can either be tailored to suit an organization’s special requirements or may be procured from the market. These data cleansing software tools have set guidelines like deleting all records that have not been updated within the last five years or so, correcting zip codes for particular towns or cities, changing the price structure of all saleable products from US Dollar to contemporary Euros. Exceptional software can perform amazingly by recovering partly lost data by activating their own search engines.

The job of data cleansing or data verifying is vital for data dependant business houses. If a client’s email address is not formatted correctly in the database, the company’s automated email system would be unable to send out the special coupons or deals particularly meant for the client, thereby causing serious damage to the company’s goodwill, apart from a loss of business. Even for offline business houses, there may be problems in reaching their clientele if accurate phone numbers are not listed in the database. And even when they are listed, they must be done is such a way so that such data can be retrieved quickly. After all, the job of data cleansing is to ensure that the data or information stored is accurate and useful, and it can be retrieved quickly.

However, when two sets of data need to work in tandem, the picture may turn slightly complex. Fancy a business house having two branches, catering more or less similar customers. Data maintenance and data cleansing assumes even more importance in all such cases.

Here the data not only needs to be accurate (that can be confirmed through data cleansing) for each branch but also should match with each other. If a customer dealing with Branch A has updated his/her phone number, the new number should appear in Branch A’s database as well as in Branch B’s database so that any message that has to delivered to the customer can be done by any of the Branches. For the same reason, data cleansing works not only to ensure that the record is accurate but also it is consistent between different records.

In today’s world, a dependable database is considered to be the backbone of any business – big or small; online or offline, exporting or importing merchandise. Accurate data can only keep that backbone strong and healthy. But human error often creeps in when storing such enormous data in an encrypted database that is the private property of the business house. Without regular cleansing or verifying, whatever you may call it, errors and slip-ups are bound to occur, leading to not only loss of business but loss of face, which is even more perilous.

As earlier mentioned, data cleansing can also be done through computer software that are available in the open market. These software or programs sometimes have amazing qualities that can even provide novel means of business administration through data processing. For instance, there are programs that not only check data for accuracy but help companies build databases which ensure the integrity and accuracy of inventory control. With such tools in hand, business houses can work with lower inventory levels and craft ‘Data-Driven’ purchasing. The programs also help business housed edit and clean available inventory data while creating an accurate baseline database.

However, data by nature is huge and generally unmanageable in the raw format. For effective analysis and examination, data needs to be pooled, both logically and in terms of volume. This process of pooling raw data, so as to make it compatible for analysis, is broadly called data integration (DI).

Data integration systems are formally defined as a triple where G is the global (or mediated) schema, S is the heterogeneous set of source schemas, and M is the mapping that maps queries between the source and the global schemas.

The data that companies get access to, generally comes from different sources, and need to be unified in order to provide end-users with a comprehensive picture. The main function of data integration is to achieve this purpose. Data integration appears with increasing frequency as the volume and the need to share existing data goes. It has been the focus of extensive theoretical work which involves numerous open problems that need to be solved.

Data integration has been an extremely important technique that has been successfully put into use for commercial, scientific and management purposes (where it is known as Enterprise Information Integration). When research results from different bioinformatics repositories need to be combined or two similar companies need to merge their databases, Data Integration is the process experts turn to.

The data integration procedure has to integrate data models and establish common terms of reference. For this, integration techniques need to go beyond simple consolidation of application databases.

Data Integration comprises of three separate layers – the data transport interface, the data exchange services, and the user/ application interface.

These components need to be successfully managed for effective integration to happen. Data integration, by nature, can also be of two types – internal integration (for pooling data available within the databases of a single company) and external integration (Integration with external constituencies in a highly-controlled environment – generally, among parties that have strong, well-established partnerships, when one or more of the parties impose standards for data exchange, or when the volume of data exchanged is very high and frequent).

There exist certain basic requirements that need to be fulfilled in order to make the data integration process a success. The major factors that influence internal data integration can be tabulated as under:

1. Relative centralization or distribution of internal business processes
2. Location of data stores within the organization,
3. Current application deployment on centralized or distributed systems, and
4. Changes in business conditions such as acquisition of a company or business unit.

The requirements for external data integration, however, are slightly different. Hence, the factors that influence it also differ from those that affect internal data integration. These factors may be denoted as follows:

1. Nature of the business relationship with the external constituency,
2. Types of shared business processes,
3. Data exchange standards imposed by the external organization(s),
4. Technology available at the external site.

Apart from these taking care of these factors that are crucial to the performance of internal and external data integration respectively, all integration procedures must comply with the organizational security policies.

Data integration techniques come with a certain, pre-estimated cost. The expenses for initialization and subsequent long-run deployment of integration also need to be considered by company heads.
Integration has, over the recent years, become an immensely useful technique for managing data in the corporate framework. Physical integration of data (involving tools and technology) has often taken center stage, and a good volume theoretical study has been done on this aspect of data integration. However, for effectively integrating available data, a comprehensive study of the strategies, designs, standards and policies of governance need to be undergone. The nature of the data that flows in the information system of a company also has to be understood thoroughly.

Indeed, for the purpose of clarification and understanding, the data integration procedure can be explained in terms of a simple framework. This framework comprises of:

• Integration structure (standards, guidelines, processes, policies, DI “rules”, integration patterns),
• Decisions of integration arrived at by means of resolution of the DI issues and data stewardship (quality standards, compliance, security),
• The plan for DI Design (research of the source data, business design, design of target integration and rules of transformation), and
• Governance and strategy that directs the current maintenance (data quality program and change management).

Hence, we find that, as businesses grow, the sheer volume of data that needs to be handled and analyzed grows enormously. Data integration provides a solution to this requirement, by explaining the procedures for effectively pooling the data. Integration of data needs to be comprehensive too, accommodating for both internal and external needs.

Data is very important for any business – big or small. That is because it is by storing, managing, retrieving and analyzing this data that the business is able to arrive at various crucial decisions such as product design and placement, product pricing, customer behaviors and preferences, market opportunities, competitor surveys and such other. Thus the importance and need for data maintenance and administration.

Data is crucial and thus the business has to decide who in the organization has access to such data – not all people may have access to all data, except of course the owner of the company. In really big companies there are groups of people who might have access to the data while such information is passed on to the subordinates by the group head. This is why the data needs to be secured and administered properly so that it can be retrieved quickly by the person who needs it and kept away from those who should not have access to it. Yes, the issue of data security goes hand in hand with data maintenance and administration.

In computing science, data maintenance and administration refers to the management and running of the organization’s data that is generally stored in the database under a chosen database management system or alternative systems like electronic spreadsheets. In smaller outfits, if one is running a single-user system, this is done by the person who owns the organization while in bigger organizations; administrators (who are often specialists and are responsible for various functions of the business including data analysis) are deputed to do the job.

Alternatively, data administration may be split between the end-users who are made responsible for maintaining their client accounts with a person designated as data administrator who is answerable to security specific information as this is shared by all the users. The ideal way for determining how to split responsibilities between the end-users however lies in finding out the data maintenance task that need to be performed through downloading certain tools. These tools become extremely handy for those who are responsible for doing the job.

Security price quote and financial information quotes are performed using the net’s scheduled automatic download feature where the data administrator becomes the person responsible for configuring the download parameters. This includes the time of the download, securities to be downloaded, etc. The data administrator is also responsible for reviewing the download logs for key data changes.

In order to gain account portfolio information like account record updates and transaction and position reconciliation data will be available (with the help of the tool) in the form of data files received from the custodian or the clearing firm. These files, however, will be introduced via the tool/net’s institutional import interface. Nevertheless, whether these introductions are best performed by the data administrator or by each end-user depends on several factors. When all or most of the advisor/ rep accounts are kept within a single file, then the introduction is best performed by the data administrator. New accounts usually do not require manual intervention by the data administrator for assigning the proper Rep ID.

When the custodian or clearing firm provides break up data files for each adviser or rep, then it becomes comparatively easier for each of them introducing their own data files. Nevertheless, an organization may still insist on assigning the task to a data administrator jus to relieve the advisor or the rep. No matter who performs this task, or whether it has been configured to crop up on its own, someone should review the data for accuracy. Under normal circumstances, this should be the duty of each adviser or rep as each of them are best placed to know what trades or transactions were placed for their clients. Often a Data Reconciliation tool comes handy in this reviewing process.

For users who do not have access to custodian or clearing firm data for some or all of their accounts and must manually enter the data from usually available sources, the decision of whether this job comes under the administrator’s task or to be performed by each adviser or rep is best left to the user.

So far as analyzing and administration of data is concerned, there are quite a good many net based solutions that not only evaluate but also indicates the various possibilities for data administration. Some of their data administration services include…

• Generation of addressing
• Separation and correction of fields
• First name review
• Existence check
• Dead list/ negative comparison
• Credit assessment
• Risk index relationship
• Splitting of consumer and business address
• Relocation address comparison
• Tracking of returns

Using automated methods of statistical analysis which may be termed as a form of data mining, business people are now in the process of finding out new trends or drifts in business related behaviors that was earlier ignored. As soon as this is achieved, it can be utilized in a prognostic manner to newer business ventures and also sometimes for the present business itself to find tune it and enhance its productivity.

But the first step towards reaching the goal, as you may appreciate, requires the right kind of data or information that is relevant to the business to be gathered. Although it may sound difficult at first, it is not that hard to get into it. After all, it is your business and you’re the best judge to see what data is pertinent to your business. If you are selling shoes, for instance, you would know who makes them best, which community prefers them more or which is the best selling season. And once these information or data is gathered, use your spare time to hone them or edit them till you reach the gist. If you are presently tracking the date of the customer in a contemporary DBMS, you have probably finished with it. That means you have already created your own data warehouse.

Now is the time for experimentation. Select one or many algorithms to match your problem with the data in hand. Algorithm, as you may understand, involves repeated step-by-step application of a process till one reaches the solution. Since you are experimenting with several methods, chances are, one of them will click. Or you may go for two of the most common forms of algorithms, namely,Regression and Classification. The first one is the commonest statistical technique adopted by those into data mining the world over. It involves selecting a numerical dataset & developing it into a mathematics formula that agrees with the data. As you feel the results are ready to be used for forecasting the behavior, take your current data, plug this to a developed formula & you have reached your goal! However, one of the major drawbacks related to this method is that the system works great with continuous data in quantities only. When you are working data that is categorical and the order is not crucial, you would do well with other techniques.

When you are using categorical data or may be a combination of continuous categorical and numeric data, Classification will suit you fine. It is quite competent in processing a much wide kind of data as compared to Regression and so is becoming more popular with the new breed of business community. Instead of pursuing a complex mathematical formula to reach a decision, it provides you with decision tree, requiring many binary decisions.

Data mining, as said earlier, is an oven fresh topic now. Apart from Classification and Regression, many other algorithms for data mining have hit the market like bomb shells now.

Products of data mining products are the big thing today. Most database distributors have taken adequate steps to see to it the platforms can use the techniques for data mining.

Oracle’s Data Mining Suite (Darwin) already includes Neural Networks, Classification and Regression trees, Regression Analysis, k-nearest neighbors and detailed Clustering Algorithms. The SQL Server of Microsoft allows data mining by using clustering algorithms and classification trees.Data mining algorithms are on offer by many statistical packages such as S-Plus, SAS and SPSS.

So, you can see that data mining is no business but much more than that. In today’s competitive world, data mining has become something very important as it enables a business to perform better and give it an edge over competition.

As assorted and diverse information structures with varied privacy policies prevail in the IT scenario covering all over the world, technical control and logging mechanisms are required to enforce and monitor privacy rules, laws and by-laws to ensure accountability for information use. While several technologies address privacy protection in enterprise IT systems, they are categorized into two primary divisions, namely, Communication and Enforcement. Policy Communication platform for Privacy Preference is P3P which is a standard for communicating privacy practices as well as matching them with the preferences of individuals. Policy Enforcement, however, covers the Extensible Access Control Mark up Language or XACML, which along with its Privacy Profile is the standard for expressing privacy policies in a machine-readable language that a software system can use in enforcing the policy in enterprise IT systems.

Though the Enterprise Privacy Authorization Language or EPAL is fairly similar to XACML, it is not an industry standard while Web Service Privacy or WS-Privacy will be a specification for communicating privacy policy in web services only.

Despite several measures taken to catch hold of electronic offenders in the United States, data privacy is not highly legislated there. Credit report generation of individuals for employment, housing, purchase of consumer durables are accepted norms in many states to which nobody seems to object, no matter how much the federal government tries to restrict the practice. Apart from meager regulations involving children’s privacy protection (Children’s Online Privacy Protection Act) and HIPPA, there is hardly any all-encompassing law in the United States, restricting the use of personal data. Probably the First Amendment that protects free speech in the US gives rise to conflicts with any attempt on part of the government to restrict privacy even in the electronic age. The matter seems to gain more momentum in the light of many countries where privacy works as a tool to suppress free speech.

In order to demonstrate compliance with the European Commission directives on corporate privacy matters, the Safe Harbor Arrangement was developed by the US Department of Commerce which tried to simplify relations between them and the European Business Community at large. However, the US Supreme Court at last granted the right of privacy to individuals in Griswold v. Connecticut but barring California, very few US states recognize or encourage an individual’s right to privacy. An unassailable right to privacy is enmeshed in the California Constitution’s Article 1, Sec.1 while the California legislature has taken several legal measures to protect the right. The California Online Privacy Protection Act of 2003 mandates all commercial websites or online services that collect personal information on California residents through web to post a privacy policy on the site as also to comply with its policy. However, with the enactment of further laws and regulation protecting the privacy of all American citizens, data privacy and security is now seeing better days in the US.

Compared to the fate of US citizens in matters relating to the right of data privacy, the average Canadian seems to fare well. The Personal Information Protection & Electronic Documents Act in Canada went into effect (in relation to federally regulated organizations) on January 1, 2001 while it became fully effective in relation to all other organizations on January 1, 2004. Such regulation has also brought Canada into compliance with the requirements of the European Commission’s directive on the subject.

The data privacy and security system in Europe, however, is far more advanced than what it is in North America. The right to data privacy is rigidly regulated in most of Europe. Right to respect for one’s ‘private and family life, his home and his correspondence’ subject to few restrictions is amply provided in Article 8 of the European Convention on Human Rights (ECHR). The European Court of Human Rights interprets the Article 8 in the light of unlawful gathering of personal information that often extends to collection of medical data or particulars of personal expenditure. Sate interference with a person’s privacy is only allowed by the Court if three conditions are fulfilled – (1) the interference is in according with local law; (2) pursues a legitimate goal and (3) it is needed in a democratic country.

Apart from legislative interference, private enterprises often posed threat to data privacy in some European nation states while automated processing of data became pervasive. In order to put a rein on such electronic crimes, the Convention for the protection of Individuals with regard to Automatic Processing of Personal Data within the Council of Europe in 1981 and accordingly:-

• The old Data Protection Act 1984 was repealed and replaced by Data Protection Act 1988 in the United Kingdom. The new law is far more stringent about maintaining and protecting the secrecy of private data.
• In Germany, both the Federal Government and the state bodies had enacted data protection legislations for the benefit of common man
• France adopted its present legal structure for providing protection of personal data that is almost foolproof.

However, experience have shown that mere enactment of law can hardly address the menace in which hackers are disrupting the functioning of significant research activities, indulging in unauthorized withdrawal of large amount of money from banks by duplicating the encryption in Debit Cards and are also selling personal information to whosoever is willing to buy.

Data warehouses prove useful to business houses for improving customer services but if quality data is not available, the whole purpose is lost. What really happens is something like this. Organizations will at first spend adequate time and energy ensuring the quality of data, but that initial importance on quality dies away as time passes, resulting in ‘dirty data’ flooding the data warehouse. And as a result, the business suffers.

Coming down to data integrity, which also means the state of its consistency and precisionMessage Authentication Code, Message Integrity Code or MAC implies some relationship with its quality. This integrity is often ensured by the using a number that is known as . With reference to security of the information in general, integrity indicates the very validity of the data which, however can be affected by malevolent altering as happens in the case of an attacker changing the account number in the bank transaction or forging of a document. Also, it can involve accidental altering through error in transmission or due to a hard disc crash.

This is why data security has today become such an important issue. To ensure that data quality is upheld, data security has to be enforced and so not all people in an organization has access to the data. There may also be cases where certain people have access to data for just a short time, and after their needs are fulfilled, the access is withdrawn.

Data integrity may be imposed in a system by a succession of rules or integrity constrains while three kinds of such constrains form a part of the rational data model namely, Domain Integrity, Referential Integrity and Entity Integrity. The first one is related to the idea of a Primary Key. The Entity Integrity clarifies that each table should have the Primary Key as also that the columns or column chosen as Primary Key must be exclusive and not a void.

The Referential Integrity relates to the idea of a Foreign Key while it clarifies that the value of any Foreign Key is only in one of the two states. The normal phenomenon suggests that the value of the Foreign Key will refer to the value of a Primary Key in the database. Often this depends on the business rules when the value of the Foreign Key would be invalid. In such cases it may be presumed the relationship does not exist between objects represented or that the relationship is of unknown quality.

So far as Domain Integrity is concerned, it needs all relational database columns to be declared to a defined domain and the primary data unit in a relational model will be the item data. Such data is atomic or Non-decomposable. A domain usually stands for a set of values that are the same and as such consists of groups of values from where the actual value appears in a column.

MD5 hash values are an example of data integrity in cryptography while the various byte blocks work as numerical summation of the data item’s content. When the data changes, MD5 hash does not give the same result.

Strange but nevertheless true, many business houses have failed and would perhaps continue to fail because the management and control of data quality is not given the priority it deserves. However most businesses today realize the value of data and thus maintaining data quality and integrity is given the importance it deserves. Of course, building an effective organization-wide data management strategy may prove to be a difficult job. However, most US trade and commerce are no more based on world-wide framework and unless adequate attention is given to maintaining data quality and integrity, even home market may collapse any day.

• Increasing the consistency and buoyancy in making decisions
• Decreasing the regulatory fine hazards
• Exploiting the income generating potential of relevant data
• Ameliorating data security
• Delegating accountability in information eminence

Data governance however, aims at improving the quality of data by way of establishing a team consisting of the leadership, business managers and data stewards who are responsible for the accuracy, consistency and totality of the available data. The team so assigned often employs some form of tactic for tracking and improving quality of data like the Six Sigma as also tools for data mapping, cleansing, profiling and also monitoring data.

Initiatives in data governance are also aimed to achieve several objectives that include transparency for the external and internal customers of a company as is done in supply chain management, complying the relevant regulatory by-laws, improving operations during corporate mergers, as also aiding the competence of knowledge employees by eradicating confusion and error factors. Instances of several data governance steps being inspired by endeavors in the past to improve information quality are not rare.

As far as implementation of data governance initiatives are concerned, they often vary in range and also in origin. A mandate may sometime arise to begin an enterprise broad enough, while another may arise to start a pilot project or several projects that are restricted in objective and scope. These are usually aimed to resolve the present issues or simply for demonstrating value. Also, often an initiative may originate at lower down the rung and would be for setting up through a limited scope for demonstrating value to the potential sponsors who are higher in the organization.

Data governance usually needs tools though it was declared at the Data Governance Conference held in Orlando, Florida in December 2006 that it mostly communication on which the topic resides. Taking this clue, vendors try to position their products as data governing tools, focusing areas of various data governing initiatives. But sad to say, they are usually found inappropriate while some of them marked governance tools actually address governance needs.

Also, in most companies, there are some areas in reference data which proves crucial for the main business. Take for example financial management where the identification of investment opportunities is of great importance. However, closer examination often reveals that the data is not the same in quality and usage all through the organization. Ingrained definitions that are specific to individual businesses complicate the issue further. Then there are political pressures and internal lack of trust that often changing the color of the given data. Data governance in such scenarios may take the form of MDM or Master Data Management program.

Sometimes, large multi-national companies utilize shared data among its white collar executives in order to market a much awaited product or services before the due date. This obviously causes strain in their existing data governance system. But how one of the world pharmaceutical leaders, Pfizer had solved the problem is given here as a case-study.

Since data governance is proving more and more popular with most medium and large organizations in the United States, this treatise will prove rather inactive if some of the major data governance organizations are not revealed here. The IBM Data Governance Council is a body that consists of companies, corporations, Technology Solution providers and institutions who have the related objective to create quality control and consistency in governance for companies to better protect their crucial data.

The DGS-COP or the Data Governance & Stewardship Community of Practice is a vender-neutral group that is open to practitioners, stakeholders, academics, consultants and vendors. It provides many helpful implements to its members that include case-studies, dashboards, maturity models and online events.

People also obtain valuable help through attending Data Governance Conferences that are held in the US as well as in Europe every year where various institutions and companies show case their models for the benefit of attendees.

Data is not anymore the simple business information of a few years back. It can mean a lot more today in this complicates market place. And that is why terms such as data governance have evolved – and we can only expect that its importance will be on the rise in the years to come.

Data Modeling

Data Modeling involves creation of a structure for the data that is to be collected and used and organizing the data in such a way as can provide easy access and efficient storage for future use in preparing reports or analysis. While creating the structure for the data, it may be given an appropriate name, demonstrating its relationship with other data and should also be classified. For example, when one is having a database of media, it might be appropriate to have an order or structure that includes images, videos or audio files. Within each category, one can easily classify objects accordingly.

Data Warehousing

Data warehousing is nothing but storing of the collected data effectively so that it may be easily accessed at a later date. Though different organizations collect data diversely, serving their individual purpose, most of them use their data more or less in the same way, when creating reports or reaching significant business decisions. Data warehousing may also be termed as a huge corporate data storage bin, encompassing a separate office or a department. Large Big companies often maintain adequate security arrangement for their data warehousing.

Data Movement

Data movement involves moving data from one location to another where some amount of caution is always needed. This is often done when the collected data is moved to a database and eventually to an end user. However, in the process, attention must be given to see that all hardware, applications and collected data are compatible to each other and the data is appropriately classified and stored so that it can be accessed easily within an organization. It is also an expensive process, requiring adequate resources to ensure that no data is misplaced or lost during transit. Transferred to a computer, it can be sent out as email attachments. Transmitted data can also take the form of a hard copy report for the management to take decisions.

Database Administration

Database administration plays a significant role in data management. Database administrators in most organizations or enterprises are responsible for maintaining the database environ and are authorized to perform some of the tasks that include (i) Recoverability; (ii) Integrity; (III) Availability; (iv) Security; (v) Performance and (vi) Development & Testing support, each one of which are detailed below.

Recoverability relates to the way the back up of stored data behaves. It needs regular testing to check its fidelity and dependability. Data integrity is also concerned with the reliability of the stored data. Data integrity is vital at the time of creating reports or during data analysis. Invalid data or data that that can not be relied upon, can never be used in any company or organization that deal with facts and figures.

A database administrator’s yet another essential task involve database security as the person is directly in charge of giving clearance as well as access to databases in an organization. Also important is availability. This may be termed as a means of ensuring that the database is up and running in the usual way. Higher will be the level of productivity when it is really up and full of the desired information. Performance of the database administration is usually seen in terms of getting the most out of the available hardware, applications and data on a given topic.

As for development and testing support, this is where a good database administrator can demonstrate his or her true mettle. A well administered database not only indicates excellent performance on part of the administrator but it also serves as an admirable base from which accurate reports can be created.

Data Mining

This is a crucial component to data management since it interprets valuable information on the data that has been collected. It is quite significant in a way that leads to interactions between various points of issue or trends that can be found useful for a business to develop and grow.

While there could hundreds of topics related to data management, they all boil down to a generalized formula starting from collection of raw data from various sources to shaping them into formatted profile from which Management Gurus make valuable decisions that often affects world economy.